API Reference

This section contains the API of the modules and functions.

Command line interface querynator.

querynator.__main__.query_api_civic(*args: Any, **kwargs: Any) → Any

querynator.__main__.query_api_cgi(*args: Any, **kwargs: Any) → Any

querynator.__main__.create_report(*args: Any, **kwargs: Any) → Any

querynator.__main__.Cancer()

Function to create instance of click.Choice EnumType with cancer types

source: https://www.cancergenomeinterpreter.org/js/cancertypes.js

Returns:

Enumeration of cancer types

Return type:

click.Choice EnumType

class querynator.__main__.EnumType(enum, case_sensitive=False)

This is a class for a click.Choice of type EnumType

convert(value, param, ctx)

Convert the value to the correct type. This is not called if the value is None (the missing value).

This must accept string values from the command line, as well as values that are already the correct type. It may also convert other compatible types.

The param and ctx arguments may be None in certain situations, such as when converting prompt input.

If the value cannot be converted, call fail() with a descriptive message.

Parameters:

• value – The value to convert.

• param – The parameter that is using this type to convert its value. May be None.

• ctx – The current context that arrived at this value. May be None.

querynator.__main__.filter_vcf_by_vep(vcf_path, logger)

Function to filter given vcf to remove synonymous and low impact variants based on VEP annotation

Parameters:

vcf_path (str) – Variant Call Format (VCF) file (Version 4.2)

Returns:

list of lists of pyVCF3 records (input file, removed, filtered)

Return type:

list

querynator.__main__.get_unique_querynator_dir(querynator_output)

add index if “querynator_results” already exists in user given out dir

Parameters:

querynator_output (str) – path to store querynator results

Returns:

unique result directory

Return type:

str

querynator.__main__.make_enum(values)

Function to create an EnumType from a dict

Parameters:

values (dict) – json/dict like object with {key: value} pairs

Returns:

enumeration

Return type:

Enum

querynator.__main__.write_vcf(vcf_template, vcf_record_list, out_name)

Function to write a vcf file from list of pyvcf3 records to result directory

Parameters:

• vcf_header (pysam header object) – pysam header object from input vcf

• vcf_record_list (list) – list of pysam records

• out_name (str) – name for the created vcf file

Returns:

None

Return type:

None

Query the cancergenomeinterpreter (CGI) via it’s Web API

querynator.query_api.cgi_api.add_cgi_metadata(url, output, original_input, genome, filter_vep)

Attach metadata to cgi query

Parameters:

• url (str) – API url with job_id

• output (str) – sample name

• filter_vep (bool) – flag whether VEP based filtering should be performed

Returns:

None

Raises:

BadZipfile

querynator.query_api.cgi_api.delete_job_cgi(url, headers, output, logger)

Delete query from the CGI server after analysis is complete

Parameters:

• url (str) – API url with job_id

• headers (dict) – Valid headers for API query

• output (str) – sample name

Raises:

Exception

querynator.query_api.cgi_api.download_cgi(url, headers, output, logger)

Download query results from cgi

Parameters:

• url (str) – API url with job_id

• headers (dict) – Valid headers for API query

• output (str) – sample name

Raises:

Exception

querynator.query_api.cgi_api.hg_assembly(genome)

Use correct assembly name

Parameters:

genome (str) – Genome build version, defaults to hg38

Returns:

genome

Return type:

str

querynator.query_api.cgi_api.query_cgi(mutations, cnas, translocations, genome, cancer, headers, logger, output, original_input, filter_vep)

Actual query to cgi

Parameters:

• mutations (str) – Variant file (vcf,tsv,gtf,hgvs)

• cnas (str) – File with copy number alterations

• translocations (str) – File with translocations

• genome (str) – Genome build version

• email (str) – To query cgi a user account is needed

• cancer (str) – Cancer type from cancertypes.js

• logger – prints info to console

• output (str) – sample name

querynator.query_api.cgi_api.status_done(url, headers, logger)

Check query status

Parameters:

• url (str) – API url with job_id

• headers (dict) – Valid headers for API query

Raises:

HTTPError

Returns:

True if query performed successfully

Return type:

bool

querynator.query_api.cgi_api.submit_query_cgi(mutations, cnas, translocations, genome, cancer, headers, logger)

Function that submits the query to the REST API of CGI

Parameters:

• mutations (str) – Variant file (vcf,tsv,gtf,hgvs)

• cnas (str) – File with copy number alterations

• translocations (str) – File with translocations

• genome (str) – CGI takes hg19 or hg38

• email (str) – To query cgi a user account is needed

• cancer (str) – Cancer type from cancertypes.js

• token (str) – user token for CGI

• logger – prints info to console

Returns:

API url with job_id

Return type:

str

Query the Clinical Interpretations of Variants In Cancer (CIViC) API via its python tool CIViCPY

querynator.query_api.civic_api.access_civic_by_coordinate(coord_dict, logger, build)

Query CIViC API for individual variants

Parameters:

• coord_list (list) – List of CoordinateQuery objects

• build (str) – reference genome

Returns:

CIViC variant objects of successfully queried variants

Return type:

list

querynator.query_api.civic_api.add_civic_metadata(out_path, input_file, search_mode, genome, filter_vep)

Attach metadata to civic query

Parameters:

• out_path (str) – Name of directory in which results are stored

• input_file (str) – path of original input file

• search_mode (str) – search mode used in CIViC Query

• filter_vep (bool) – flag whether VEP based filtering should be performed

Returns:

None

Return type:

None

querynator.query_api.civic_api.append_to_dict(dict1, dict2)

appends values of a dictionary to another dictionary with lists as values :param dict1: dictionary to append to :type dict1: dict :param dict2: dictionary with values to append :type: dict2: dict :return: appended dict :rtype: dict

querynator.query_api.civic_api.check_vcf_input(vcf_path, logger)

Checks whether input is vcf-file with all necessary columns.

Parameters:

vcf_path (str) – Variant Call Format (VCF) file (Version >= 4.0)

Returns:

None

querynator.query_api.civic_api.concat_dicts(coord_id_dict, variant_obj, filter_vep)

Create and combine different dictionaries created for single CIViC variant object

Parameters:

• coord_obj (CIViC CoordinateQuery Object) – CoordinateQuery Object to respective variant object

• variant_obj – single CIViC variant object

Returns:

All information for respective CIViC variant object

Return type:

dict

querynator.query_api.civic_api.create_civic_results(variant_list, out_path, logger, filter_vep)

Combine result dictionaries of all CIViC variant objects to a table and write it to user-specified file

Parameters:

• variant_list (list) – List of CIViC variant objects of successfully queried variants

• out_path (str) – Name for directory in which result-table will be stored

• filter_vep (bool) – flag whether VEP based filtering should be performed

Returns:

None

Return type:

None

querynator.query_api.civic_api.get_assertion_information_from_variant(variant_obj)

Get all assertion information from a single CIViC variant object

Parameters:

variant_obj – single CIViC variant object

Returns:

Assertion information for respective CIViC variant object

Return type:

dict

querynator.query_api.civic_api.get_coordinates_from_vcf(input, build, logger)

Read in vcf file using “pyVCF3”, creates CoordinateQuery objects for each variant. This function does find (ref-alt): SNPs (A-T) DelIns (AA-TT) Deletions (TTTCA - AT)

Parameters:

• input (list or str) – list of pyVCF3 records or vcf file to query

• build (str) – reference genome

Returns:

CoordinateQuery objects

Return type:

list

querynator.query_api.civic_api.get_evidence_information_from_variant(variant_obj)

Get all evidence from a single CIViC variant object

Parameters:

variant_obj – single CIViC variant object

Returns:

Evidence information for respective CIViC variant object

Return type:

dict

querynator.query_api.civic_api.get_gene_information_from_variant(variant_obj)

Get all gene information from a single CIViC variant object

Parameters:

variant_obj – single CIViC variant object

Returns:

Gene information for respective CIViC variant object

Return type:

dict

querynator.query_api.civic_api.get_molecular_profile_information_from_variant(variant_obj)

Get all molecular profile information from a single CIViC variant object

Parameters:

variant_obj – single CIViC variant object

Returns:

Molecular profile information for respective CIViC variant object

Return type:

dict

querynator.query_api.civic_api.get_positional_information_from_coord_obj(coord_obj)

Get information about the position of the variant in the genome

Parameters:

coord_obj (CIViC CoordinateQuery Object) – CoordinateQuery Object to respective variant object

Returns:

Positional information for respective CIViC variant object

Return type:

dict

querynator.query_api.civic_api.get_querynator_id(querynator_id)

Get the querynator id in dict format

Parameters:

querynator_id (str) – Querynator id

Returns:

Querynator id for respective CIViC variant object

Return type:

dict

querynator.query_api.civic_api.get_variant_information_from_variant(variant_obj)

Get all variant information from a single CIViC variant object

Parameters:

variant_obj – single CIViC variant object

Returns:

Variant information for respective CIViC variant object

Return type:

dict

querynator.query_api.civic_api.query_civic(vcf, out_path, logger, input_file, genome, filter_vep)

Command to query the CIViC API

Parameters:

• vcf (str or list) – Variant Call Format (VCF) file (Version 4.2) or list of pyVCF3 variant records

• out_path (str) – Name for directory in which result-table will be stored

• input_file (str) – path of original input file

• filter_vep (bool) – flag whether VEP based filtering should be performed

Returns:

None

Return type:

None

querynator.query_api.civic_api.smoothen_dict(dict, s)

makes string out of lists

Parameters:

• dict (dict) – dict with lists as values

• s (bool) – True if string and special string character needed

Returns:

dict with strings as values

Return type:

dict

querynator.query_api.civic_api.sort_coord_list(coord_dict)

Sort the input list to the bulk search

Parameters:

coord_list (list) – List of CoordinateQuery objects

Returns:

sorted coordinates

Return type:

list

querynator.query_api.civic_api.sort_rules(s)

Set rules to correctly sort chromosomes X,Y,M

Parameters:

s (str) – “string” chromosome (X,Y,M)

Returns:

integer to sort by

Return type:

int

querynator.query_api.civic_api.vcf_file(vcf_path)

Checks whether input is vcf-file.

Parameters:

vcf_path (str) – Variant Call Format (VCF) file (Version 4.2)

Returns:

None

Create one report of the querynator results and individual reports for each variant

querynator.report_scripts.create_report.add_variant_name_report(df)

Adds a column with a name of the variant for the report to the df

Parameters:

df (pandas DataFrame) – result df

Returns:

List of variant names

Return type:

list

querynator.report_scripts.create_report.assign_comb_evidence_labels(row)

Assign the evidence labels for each Knowledgebase

Parameters:

row (pandas DataFrame row) – Row of the variant dataframe

Returns:

Evidence labels for each Knowledgebase

Return type:

str

querynator.report_scripts.create_report.check_if_nan(value)

Checks if a value is NaN and returns an empty string if it is. :param value: The value to check. :type value: str :return: The value if it is not NaN, otherwise an empty string. :rtype: str

querynator.report_scripts.create_report.create_barplot(input, title, out_path)

Creates and saves a barplot as png

Parameters:

• input (pandas.DataFrame) – input dataframe

• title (str) – title of the plot

• out_path (str) – output path

Returns:

matplotlib figure

Return type:

matplotlib figure

querynator.report_scripts.create_report.create_evidence_table(row, width_dict)

Creates a table containing CIViC evidence information for a specific variant. :param row: The row of the dataframe. :type row: pandas.Series :param width_dict: A list containing the width of each column. :type width_dict: list

querynator.report_scripts.create_report.create_html_link(s)

Creates a clickable link from a string. Used to convert file path into clickable form.

Parameters:

s (str) – The string to create a link from.

Returns:

The string as a clickable link.

Return type:

str

querynator.report_scripts.create_report.create_link_col(row, report_path)

Creates a link to the individual report to display in the overall report

Parameters:

• row (pandas Series) – row of the df

• report_path (str) – path to the directory in which the individual reports will be saved in

Returns:

link to the individual report

Return type:

str

querynator.report_scripts.create_report.create_report_htmls(outdir, basename, civic_path, logger)

Creates the overall report for all variants and the individual reports for each variant

Parameters:

• outdir (str) – Path to report directory

• basename (str) – User given Project name

Civic_path:

Path to civic results

Returns:

None

Return type:

None

querynator.report_scripts.create_report.create_therapy_table(row, response, width_dict, biomarkers_df)

Creates a HTML table containing all Therapy & Drug related information provided by CGI for a specific Protein Change.

Parameters:

• row (pandas.Series) – The row of the dataframe.

• response (str) – The response to a specific drug.

• width_dict (list) – A list containing the width of each column.

• biomarkers_df (pandas.DataFrame) – The biomarkers dataframe from CGI.

Returns:

A HTML table containing all Therapy & Drug related information provided by CGI for a specific Protein Change.

Return type:

HTML table

querynator.report_scripts.create_report.create_tier_table(df, tier, report_path)

Creates a table for the report for the given tier

Parameters:

• df (pandas DataFrame) – df containing all variants

• tier (int) – tier to create the table for

• report_path (str) – path to the directory in which the individual reports will be saved in

Returns:

df containing the variants of the given tier

Return type:

pandas DataFrame

querynator.report_scripts.create_report.create_upsetplots(df, out_path)

Create upsetplot of (1) the number of variants per Knowledgebase and (2) the number of variants per tier

Parameters:

• df (pandas.DataFrame) – Variant dataframe

• out_path (str) – Path to output directory

Returns:

List of Upsetplot figures

Return type:

list

querynator.report_scripts.create_report.encode_upsetplot(fig)

Encodes an upsetplot figure as base64

Parameters:

fig (matplotlib figure) – Upsetplot figure

Returns:

Encoded upsetplot figure as string to add to report

Return type:

str

querynator.report_scripts.create_report.get_KB_count(df)

Get the count of variants for each Knowledgebase to add to the pieplot

Parameters:

df (pandas DataFrame) – Variant dataframe

Returns:

Count of variants for each Knowledgebase

Return type:

pandas DataFrame

querynator.report_scripts.create_report.get_disease_names_CIViC(row)

Get CIViC’s disease names for the variant

Parameters:

row (pandas DataFrame row) – Row of the variant dataframe

Returns:

Disease names for the variant

Return type:

str

querynator.report_scripts.create_report.get_evidence_description(row)

takes in string of evidence descriptions and returns them as a HTML list

Parameters:

row (pandas.core.series.Series) – row of the dataframe

Returns:

string of evidence descriptions

Return type:

str

querynator.report_scripts.create_report.get_reference_build(metadata_path)

Gets the reference build from the metadata file. :param metadata_path: The path to the metadata file. :type metadata_path: str :return: The reference build. :rtype: str

querynator.report_scripts.create_report.get_sources(row)

Get the Knowledgebases containing information about the variant

Parameters:

row (pandas DataFrame row) – Row of the variant dataframe

querynator.report_scripts.create_report.get_therapy_information_CGI(row, biomarkers_df, response, width_dict)

Gets all associated disease names of a specific variant. :param row: The row of the dataframe. :type row: pandas.Series :param biomarkers_df: The biomarkers dataframe from CGI. :type biomarkers_df: pandas.DataFrame :param response: The response to a specific drug. :type response: str :param width_dict: A list containing the width of each column. :type width_dict: list :return: A pandas DataFrame containing all Therapy & Drug related information provided by CGI for a specific Protein Change. :rtype: pandas.DataFrame

querynator.report_scripts.create_report.get_therapy_names(row, civic_only)

Get the therapy names for the variant

Parameters:

row (pandas DataFrame row) – Row of the variant dataframe

querynator.report_scripts.create_report.remove_dups(row)

Removes duplicates from string

Parameters:

row (pd Series) – row of pd DataFrame

Returns:

row without duplicates

Return type:

pd Series

querynator.report_scripts.create_report.retrieve_info_from_row(row, biomarkers_df, metadata_path)

This function retrieves the information from a row of the merged dataframe and returns a dictionary with the information for the report of a specific variant.

Parameters:

• row (pandas.core.series.Series) – row of the dataframe

• biomarkers_df (pandas.core.frame.DataFrame) – dataframe with all biomarkers linked to a specific variant

• metadata_path (str) – The path to the metadata file.

Returns:

dictionary with the information for the report of a specific variant

Return type:

dict

querynator.report_scripts.create_report.save_plot(input, title, out_path)

Creates and saves a upsetplot figure as png

Parameters:

• input (pandas.DataFrame) – input dataframe

• title (str) – title of the plot

• out_path (str) – output path

Returns:

matplotlib figure

Return type:

matplotlib figure

querynator.report_scripts.create_report.split_cols(col, col_name)

splits specific string cols differently

Parameters:

• col – The column of the dataframe.

• col_name – the name of the column

Type:

col_name: str

Returns:

Split column

Return type:

pandas.Series

querynator.report_scripts.create_report.write_individual_report(row, template_html, report_path, biomarkers_df, metadata_path)

This function creates a report for a specific variant.

Parameters:

• row (pandas.core.series.Series) – row of the dataframe

• template_html (str) – path to the template html file

• report_path (str) – path to the individual report directory

• biomarkers_df (pandas.core.frame.DataFrame) – dataframe with the biomarkers linked to the therapies

• metadata_path (str) – path to the metadata file

Returns:

None

Return type:

None

querynator.report_scripts.create_report.write_overall_report(template_html, report_html, fig_kb, fig_tiers, tier_table_list)

Create the overall report for the querynator results

Parameters:

• template_html (str) – Path to the template html file

• report_html (str) – Path to the report html file

• fig_kb (str) – Path to upset plot of the kb distribution

• fig_tiers (str) – Path to upset plot of the tier distribution

• tier_table_list (list) – List of pretty-html-tables of the tier tables

Returns:

None

Return type:

None

Combine the results of the CGI query with the initial VEP annotation

querynator.report_scripts.combine_cgi.combine_cgi(cgi_path, outdir, logger)

Command to combine the cgi results with the vcf’s VEP annotation

Parameters:

• cgi_path (str) – Path to a CGI result folder generated using the querynator

• outdir (str) – Path to report directory

Returns:

None

Return type:

None

querynator.report_scripts.combine_cgi.extract_coords(row)

extracts coordinates from the hgvs notation provided in “alterations.tsv”

Parameters:

row (pandas Series) – row of a pandas DataFrame

Returns:

extracted coordinates

Return type:

pandas Series

querynator.report_scripts.combine_cgi.get_all_alterations(row)

extract only the alteration strings from the “Alterations” col in biomarkers.tsv

Parameters:

row (pandas Series) – row of a pandas DataFrame

Returns:

link of biomarker to all related alterations

Return type:

list

querynator.report_scripts.combine_cgi.get_highest_evidence(row, biomarkers_linked)

get highest associated CGI evidence of the current alteration (A-D) from the biomarkers datafrane

Parameters:

• row (pandas Series) – row of a pandas DataFrame

• biomarkers_linked (pandas DataFrame) – pd DataFrame of the projects “biomarkers.tsv”

Returns:

highest associated evidence

Return type:

str

querynator.report_scripts.combine_cgi.link_biomarkers(biomarkers_df)

add alteration-link column to “biomarkers.tsv”

Parameters:

biomarkers_df (pandas DataFrame) – pd DataFrame of the projects “biomarkers.tsv”

Returns:

DataFrame of biomarkers with additional alteration-link col

Return type:

pandas DataFrame

querynator.report_scripts.combine_cgi.merge_alterations_vep(vep_df, alterations_df)

merge vep and CGI alterations annotations for each variant based on positional information (chr, pos, ref, alt)

Parameters:

• vep_df (pandas DataFrame) – DataFrame of variants and their VEP annotation

• alterations_df (pandas DataFrame) – DataFrame of variants and their CGI alterations annotations

Returns:

merged DataFrame of variants and their VEP & CGI alterations annotations

Return type:

pandas DataFrame

querynator.report_scripts.combine_cgi.read_filtered_vcf(filtered_vcf)

Create a table containing the VEP annotation of each variant and positional information to connect to the alterations.tsv

Parameters:

filtered_vcf (str) – Path to the project’s VEP filtered vcf

Returns:

vep table

Return type:

pandas DataFrame

querynator.report_scripts.combine_cgi.read_modify_alterations(alterations_path)

reads in and adds positional information to alterations file

Parameters:

alterations_path (str) – Path to alterations file

Returns:

None

Return type:

None

querynator.report_scripts.combine_cgi.remove_prefix(s, prefix)

removes prefix of a string

Parameters:

• s (str) – string which prefix should be removed

• prefix (str) – prefix to remove from string

Returns:

string with prefix removed

Return type:

str

querynator.report_scripts.combine_cgi.subset_alterations(df)

subset alterations file to only include relevant columns :param df: alterations DataFrame :type df: pandas DataFrame :return: subsetted alterations DataFrame :rtype: pandas DataFrame

Combine the results of the CIViC query with the initial VEP annotation

querynator.report_scripts.combine_civic.combine_civic(civic_path, outdir, logger)

Command to combine the civic results with the vcf’s VEP annotation

Parameters:

• civic_path (str) – Path to a CIViC result folder generated using the querynator

• outdir (str) – Path to report directory

Returns:

None

Return type:

None

querynator.report_scripts.combine_civic.merge_civic_vep(vep_df, civic_df)

merge vep and civic annotation for each variant based on the Querynator ID

Parameters:

• vep_df (pandas DataFrame) – DataFrame of variants and their VEP annotation

• civic_df (pandas DataFrame) – DataFrame of variants and their CIViC annotation

Returns:

merged DataFrame of variants and their VEP & CIViC annotation

Return type:

pandas DataFrame

querynator.report_scripts.combine_civic.read_civic_results(civic_results)

Read in the project’s CIViC annotation created by the querynator

Parameters:

civic_results (str) – Path to the project’s CIViC annotation

Returns:

DataFrame of CIViC resu

Return type:

pandas DataFrame

querynator.report_scripts.combine_civic.read_filtered_vcf(filtered_vcf)

Create a table containing the VEP annotation of each variant

Parameters:

filtered_vcf (str) – Path to the project’s VEP filtered vcf

Returns:

vep table

Return type:

pandas DataFrame

Combine CIViC-VEP with CGI-VEP

querynator.report_scripts.combine_cgi_civic.combine_cgi_civic(outdir, logger)

Combine cgi-vep table with the civic-vep table

Parameters:

outdir (str) – Path to report directory

Returns:

None

Return type:

None

querynator.report_scripts.combine_cgi_civic.merge_civic_cgi(alterations_vep, civic_vep)

merge CIViC and CGI alterations annotations for each variant based on the similar variant VEP annotation

Parameters:

• alterations_vep (pandas DataFrame) – DataFrame of variants and their VEP and CGI alterations annotations

• civic_vep (pandas DataFrame) – DataFrame of variants and their VEP and CIViC alterations annotations

Returns:

merged DataFrame of variants and their VEP & CIViC & CGI alterations annotations

Return type:

pandas DataFrame

Sort the variants into tiers (1-4) and provide a score for each variant to sort them within the tiers. The procedure is based on the Standards and Guideline provided by the AMP (Association for Molecular Pathology) and described by Li etal. (Li MM etal. Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn. 2017 Jan;19(1):4-23. doi: 10.1016/j.jmoldx.2016.10.002. PMID: 27993330; PMCID: PMC5707196.)

querynator.report_scripts.sort_variants.add_tiers_and_scores_to_df(outdir, logger)

Assigning variants from combined CGI-CIViC-VEP table to tiers and give them a score to rank them in these tiers

Parameters:

outdir (str) – Path to report directory

Returns:

None

Return type:

None

querynator.report_scripts.sort_variants.check_nan_in_pair(pair)

check if one of the values is nan

Parameters:

pair – list of values that are either string or nan

Returns:

True, index of the nan value and index of the non-nan value

Return type:

list

querynator.report_scripts.sort_variants.extract_num(s)

extracts the number from a string with pattern (e.g.) “benign(0.001)”

Parameters:

s (str) – string to extract number from

Returns:

extracted number

Return type:

float

querynator.report_scripts.sort_variants.generate_allele_freq_score(af, gnomad)

generates the variantMTB score for the allele frequency of a specific variant

Parameters:

• af (str) – a variant’s associated allele frequencies

• gnomad (str) – a variant’s associated gnomAD frequencies

Returns:

allele frequency score

Return type:

int

querynator.report_scripts.sort_variants.generate_consequence_score(cgi_consequence, civic_consequence)

generates the variantMTB score for the consequence of a specific variant

Parameters:

• cgi_consequence (str) – a variant’s associated consequence provided by CGI

• civic_consequence (str) – a variant’s consequence as given by CIViC

Returns:

consequence score

Return type:

int

querynator.report_scripts.sort_variants.generate_evidence_score(evidence_col)

generates the variantMTB score for the evidence level of one of the Knowledgebases of a specific variant

Parameters:

evidence_col – evidence value of one of the Knowledgebases

Returns:

evidence score

Return type:

int

querynator.report_scripts.sort_variants.generate_pathogenicity_score_score(sift, polyphen)

generates the variantMTB score for the pathogenicity scores (SIFT & PolyPhen2) of a specific variant

Parameters:

• af (str) – a variant’s associated allele frequencies

• gnomad (str) – a variant’s associated gnomAD frequencies

Returns:

allele frequency score

Return type:

int

querynator.report_scripts.sort_variants.get_allele_freq_tiering(row)

checks if AF is < 0.01 to assign to tier 3 (true) or 4 (false) :param row: row of a pandas DataFrame :type row: pandas Series :return: True if AF < 0.01, False if AF > 0.01 :rtype: bool

querynator.report_scripts.sort_variants.get_cgi_consequence_score(cgi_consequence)

Scores the CGI consequence

Returns:

CGI consequence score

Return type:

int

Parameters:

civic_consequence (str) – a variant’s consequence as given by CIViC

querynator.report_scripts.sort_variants.get_civic_consequence_score(civic_consequence)

Translates the CIViC consequence (variant type) (https://civic.readthedocs.io/en/latest/model/variants/types.html) nomenclature in the CGI/VEP nomenclature when possible and scores the variant

Parameters:

civic_consequence (str) – a variant’s consequence as given by CIViC

Returns:

CIViC consequence score

Return type:

int

querynator.report_scripts.sort_variants.get_consequence_score(consequence_str)

Returns the highest score of the associated CIViC variant types for a variant

Parameters:

consequence_str (str) – a variant’s consequence

Returns:

consequence score

Return type:

int

querynator.report_scripts.sort_variants.get_largest_af(af_string)

extracts the largest allele frequency from a string of allele frequencies. If single str is given, gives it out as int

Parameters:

af_string (str) – comma-separated string of allele frequencies (af)

Returns:

largest af in string

Return type:

int

querynator.report_scripts.sort_variants.get_largest_path_score(ps_string, ps_score)

extracts the largest pathogenicity score from a string of pathogenicity scores. If single str is given, gives it out as int

Parameters:

• af_string (str) – comma-separated string of pathogenicity scores

• ps_score (str) – respective pathogenicity score (SIFT or PolyPhen2)

Returns:

largest af in string

Return type:

int

querynator.report_scripts.sort_variants.get_min(e)

returns minimum of an input

Parameters:

e (str) – string or np.nan

Returns:

min of string or np.nan

Return type:

str

querynator.report_scripts.sort_variants.scoring_variants(row)

adds score to each variant to rank them within their tiers.

Parameters:

row (pandas Series) – row of a pandas DataFrame

Returns:

variant’s score

Return type:

int

querynator.report_scripts.sort_variants.subset_variants_into_tiers(row)

decides tier (1-4) for specific variant

Parameters:

row (pandas Series) – row of a pandas DataFrame

Returns:

variant’s tier

Return type:

str